Prediction, Risk Assessment and Comparison of Selected Emerging Markets' Stock Indices During COVID-19 Pandemic Using the Coherent Measure: Comparing Selected Emerging Markets' Stock Indices

In this study, we modeled the log-return of three emerging markets' stock indices, namely, Shanghai SSE, Russia MOEX, and Bombay Stock Exchange Sensex using the generalized hyperbolic family of distributions. We found the generalized hyperbolic family of distributions as the best fit for describing the probability density based on AIC and likelihood ratio test. The coherent risk measure, i.e., the expected shortfall, predicted using the best fit probability distribution, was used as a market risk quantification metric. During the COVID-19 period, the Indian stock market showed maximum market risk, followed by the Russian. The Chinese market showed the least market risk. Our experiment demonstrated a significant (p = 0.000) difference in the three markets concerning the coherent risk at different probability levels from 0.001 to 0.05 in the COVID-19 period using the Jonckheere-Terpstra test. The coherent market risk increased substantially in the Indian and Russian markets during the COVID-19 pandemic compared to the pre-COVID-19 period. However, in the Chinese market, we found that the coherent risk decreased during the COVID-19 period compared to the pre-COVID-19 period. We carried out the empirical study using the adjusted daily closing values of SSE, MOEX, and Sensex from July 2018 to July 2021 and dividing the data sets into pre-COVID-19 and COVID-19 periods based on the first emergence of the COVID-19 case.

Stock Markets Reaction to COVID-19: Evidence from Time-Varying Cointegration, Leveraged Bootstrap Causality and Event Analysis br

This paper examined the interconnectedness of COVID-19 and stock markets in some of th e most affected countries-USA, Italy, Spain and Germany. To this end, a time-varying cointegration technique was first employed to examine for the presence of comovementsbetween daily infections and stock market changes. A time-varying wild bootstrap likelihood ratio test was then employed to determine whether COVID-19 is a significant predictor of stock market performance. Lastly, an event study analysis was conducted to investigate the short-term effect of the outbreak on stock market returns. Findings revealed the existence of comovements between COVID-19 infections and stock price indices in all the selected countries. The rejection of the null hypothesis of no predictability was also recorded in all of the countries sampled. The event study analysis revealed that significant negative cumulative abnormal returns were predominant in all the countries. The reactions of the stock markets of the three European Union member countries included in the study to the pandemic are quite similar, suggesting that countries that are regionally and economically integrated are likely to experience relatively similar effects. The USA stock market was the most resilient to the impact of the outbreak

Likelihood Ratio Tests for Lorenz Dominance

In testing hypotheses pertaining to Lorenz dominance (LD), researchers have examined second- and third-order stochastic dominance using empirical Lorenz processes and integrated stochastic processes with the aid of bootstrap analysis. Among these topics, analysis of third-order stochastic dominance (TSD) based on the notion of risk aversion has been examined using crossing (generalized) Lorenz curves. These facts motivated the present study to characterize distribution pairs displaying the TSD without second-order (generalized Lorenz) dominance. It further motivated the development of likelihood ratio (LR) goodness-of-fit tests for examining the respective hypotheses of the LD, crossing (generalized) Lorenz curves, and TSD through approximate Chi-squared distributions. The proposed LR tests were assessed using simulated distributions, and applied to examine the COVID-19 regional death counts of bivariate samples collected by the World Health Organization between March 2020 and February 2021.

Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.

Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

Prevalence assessment adjusted for laboratory test performance using an example of the COVID-19 serological tests

Assessment of the prevalence of the disease or condition should consider the accuracy of the diagnostic tests. In the context of the new coronavirus infection (COVID-19) pandemic, laboratory testing has been one of the most important components of the overall strategy for the control and prevention of this infection. Seroprevalence studies have been used to assess and monitor the level of population immunity to the virus. In this paper we provide detailed description of the methods to calculate and interpret the accuracy of laboratory tests as well as their sensitivity, specificity, positive-and negative prognostic values of laboratory tests using seroprevalence of COVID-19 studies as an example for better understanding of the methodological issues. The use of the laboratory tests accuracy in prevalence studies has been demonstrated. A sample syntax to calculate confidence intervals for the prevalence estimates using the bootstrap procedure with known absolute values of true positive and true negative results, false positive and false negative results for R software is also provided. Presentation of the prevalence estimates adjusted for test performance indicators with confidence intervals improves comparability of the findings obtained using different serological tests. The article is intended for undergraduate-, postgraduate-, and doctoral students in health sciences working with the assessment of the prevalence (seroprevalence) of diseases or conditions through population-based serological surveys. © 2022, Northern State Medical University. All rights reserved.

SPATIAL ANALYSIS OF CORONA VIRUS (COVID-19) DISEASE

Coronavirus disease (COVID-19) has been quickly spreading all over the world. As of 27th September 2020, a total of 382835 confirmed cases and 19755 deaths have been reported in Uttar Pradesh. The first case in India was registered on 30th January 2020. The data of Coronavirus cases in India and state-wise is available on the Ministry of Health and Family Welfare, Govt. of India. This paper aims to identify the Hot-spots (high rate cluster) of Coronavirus disease in Uttar Pradesh through the Scan Statistics methodology of clustering using the datasets till 27th September, 2020. The clusters (group of states) are reported through scan statistic using SaTscan software. We have identified the statistically significant clusters. The scanning of corona cases is done using simulation to detect the hotspot. The Poisson distribution is assumed for the corona cases. The expected and observed number of cases are compared through the likelihood ratio test. The highest value of the likelihood ratio among all is the hot-spot (most likely cluster). The results could be pretty helpful to the Government for taking strict actions for control, spread and effective management of medical resources in the country on a priority basis since the resources are very limited. © 2022 DAV College. All rights reserved.

Clinicians’ Probability Calculator to convert pre-test to post-test probability of SARS-CoV-2 infection based on method validation from each laboratory

Background Despite best efforts, false positive and false negative test results for SARS-CoV-2 are unavoidable. Likelihood ratios convert a clinical opinion of pre-test probability to post-test probability, independently of prevalence of disease in the test population. Methods The authors examined results of PPA (Positive Percent Agreement, sensitivity) and NPA (Negative Percent Agreement, specificity) from 73 laboratory experiments for molecular tests for SARS-CoV-2 as reported to the FIND database, and for two manufacturers’ claims in FDA EUA submissions. PPA and NPA were converted to likelihood ratios to calculate post-test probability of disease based on clinical opinion of pre-test probability. Confidence intervals were based on the number of samples tested. An online calculator was created to help clinicians identify false-positive, or false-negative SARS-CoV-2 test results for COVID-19 disease. Results Laboratory results from the same test methods did not mirror each other or the manufacturer. Laboratory studies showed PPA from 17% to 100% and NPA from 70.4% to 100%. The number of known samples varied 8 to 675 known patient samples, which greatly impacted confidence intervals. Conclusion Post-test probability of the presence of disease (true-positive or false-negative tests) varies with clinical pre-test probability, likelihood ratios and confidence intervals. The Clinician’s Probability Calculator creates reports to help clinicians estimate post-test probability of COVID-19 based on the testing laboratory’s verified PPA and NPA. © 2021 International Federation of Clinical Chemistry and Laboratory Medicine. All rights reserved.

The correlation between point-of-care ultrasound and digital tomosynthesis when used with suspected COVID-19 pneumonia patients in primary care

BackgroundThe use of lung ultrasound (LU) with COVID-19 pneumonia patients should be validated in the field of primary care (PC). Our study aims to evaluate the correlation between LU and radiographic imaging in PC patients with suspected COVID-19 pneumonia.MethodsThis observational, prospective and multicentre study was carried out with patients from a PC health area whose tests for COVID-19 and suspected pneumonia had been positive and who then underwent LU and a digital tomosynthesis (DT). Four PC physicians obtained data regarding the patients’ symptoms, examination, medical history and ultrasound data for 12 lung fields: the total amount of B lines (zero to four per field), the irregularity of the pleural line, subpleural consolidation, lung consolidation and pleural effusion. These data were subsequently correlated with the presence of pneumonia by means of DT, the need for hospital admission and a consultation in the hospital emergency department in the following 15 days.ResultsThe study was carried out between November 2020 and January 2021 with 70 patients (40 of whom had pneumonia, confirmed by means of DT). Those with pneumonia were older, had a higher proportion of arterial hypertension and lower oxygen saturation (sO2). The number of B lines was higher in patients with pneumonia (16.53 vs. 4.3, p < 0.001). The area under the curve for LU was 0.87 (95% CI 0.78–0.96, p < 0.001), and when establishing a cut-off point of six B lines or more, the sensitivity was 0.875 (95% CI 0.77–0.98, p < 0.05), the specificity was 0.833 (95% CI 0.692–0.975, p < 0.05), the positive-likelihood ratio was 5.25 (95% CI 2.34–11.79, p < 0.05) and the negative-likelihood ratio was 0.15 (95% CI 0.07–0.34, p < 0.05). An age of ≥ 55 and a higher number of B lines were associated with admission. Patients who required admission (n = 7) met at least one of the following criteria: ≥ 55 years of age, sO2 ≤ 95%, presence of at least one subpleural consolidation or ≥ 21 B lines.ConclusionsLU has great sensitivity and specificity for the diagnosis of COVID-19 pneumonia in PC. Clinical ultrasound findings, along with age and saturation, could, therefore, improve decision-making in this field.

Tze Leung Lai and Anna Choi's contribution to the discussion of 'Testing by betting: A strategy for statistical and scientific communication' by Glenn Shafer

Comments on an article by Glenn Shafer (see record 2021-44219-001). It is exciting to follow Glenn Shafer's investigations into forecasting, betting, reasoning with uncertainty and foundational issues in probability, beginning with his 1973 PhD thesis at Princeton and culminating in Shafer on the Dempster-Shafer theory of belief functions, and its evolution during the past five decades to the present paper on betting scores and game-theoretic probability. Betting scores are particularly relevant in this momentous year of intensive global search for COVID19 vaccines and treatments, and upcoming presidential and congressional elections in the United States, about which pundits keep giving time-varying forecasts of the outcomes while betting markets on presidential election odds have been particularly active, similar to online sports betting markets. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Statistical Inferences: Based on Exponentiated Exponential Model to Assess Novel Corona Virus (COVID-19) Kerala Patient Data

In this article, we use exponentiated exponential distribution as a suitable statistical lifetime model for novel corona virus (covid-19) Kerala patient data. The suitability of the model has been followed by different statistical tools like the value of logarithm of likelihood, Kolmogorov–Smirnov distance, Akaike information criterion, Bayesian information criterion. Moreover, likelihood ratio test and empirical posterior probability analysis are performed to show its suitability. The maximum-likelihood and asymptotic confidence intervals for the parameters are derived from Fisher information matrix. We use the Markov Chain Monte Carlo technique to generate samples from the posterior density function. Based on generated samples, we can compute the Bayes estimates of the unknown parameters and can also construct highest posterior density credible intervals. Further we discuss the Bayesian prediction for future observation based on the observed sample. The Gibbs sampling technique has been used for estimating the posterior predictive density and also for constructing predictive intervals of the order statistics from the future sample.

Aetiology of fever in returning travellers and migrants: a systematic review and meta-analysis.

BACKGROUND: Numerous publications focus on fever in returning travellers, but there is no known systematic review considering all diseases, or all tropical diseases causing fever. Such a review is necessary in order to develop appropriate practice guidelines. OBJECTIVES: Primary objectives of this review were (i) to determine the aetiology of fever in travellers/migrants returning from (sub) tropical countries as well as the proportion of patients with specific diagnoses, and (ii) to assess the predictors for specific tropical diseases. METHOD: Embase, MEDLINE and Cochrane Library were searched with terms combining fever and travel/migrants. All studies focusing on causes of fever in returning travellers and/or clinical and laboratory predictors of tropical diseases were included. Meta-analyses were performed on frequencies of etiological diagnoses. RESULTS: 10 064 studies were identified; 541 underwent full-text review; 30 met criteria for data extraction. Tropical infections accounted for 33% of fever diagnoses, with malaria causing 22%, dengue 5% and enteric fever 2%. Non-tropical infections accounted for 36% of febrile cases, with acute gastroenteritis causing 14% and respiratory tract infections 13%. Positive likelihood ratios demonstrated that splenomegaly, thrombocytopenia and hyperbilirubinemia were respectively 5-14, 3-11 and 5-7 times more likely in malaria than non-malaria patients. High variability of results between studies reflects heterogeneity in study design, regions visited, participants' characteristics, setting, laboratory investigations performed and diseases included. CONCLUSION: Malaria accounted for one-fifth of febrile cases, highlighting the importance of rapid malaria testing in febrile returning travellers, followed by other rapid tests for common tropical diseases. High variability between studies highlights the need to harmonize study designs and to promote multi-centre studies investigating predictors of diseases, including of lower incidence, which may help to develop evidence-based guidelines. The use of clinical decision support algorithms by health workers which incorporate clinical predictors, could help standardize studies as well as improve quality of recommendations.

A computational analysis of molecular evolution for virulence genes of zoonotic novel coronavirus (COVID-19).

Zoonotic Novel coronavirus disease 2019 (COVID-19) is highly pathogenic and transmissible considered as emerging pandemic disease. The virus belongs from a large virus Coronaviridae family affect respiratory tract of animal and human likely originated from bat and homology to SARA-CoV and MERS-CoV. The virus consists of single-stranded positive genomic RNA coated by nucleocapsid protein. The rate of mutation in any virulence gene may influence the phenomenon of host radiation. We have studied the molecular evolution of selected virulence genes (HA, N, RdRP and S) of novel COVID-19. We used a site-specific comparison of synonymous (silent) and non-synonymous (amino acid altering) nucleotide substitutions. Maximum Likelihood genealogies based on differential gamma distribution rates were used for the analysis of null and alternate hypothesis. The null hypothesis was found more suitable for the analysis using Likelihood Ratio Test (LRT) method, confirming higher rate of substitution. The analysis revealed that RdRP gene had the fastest rate evolution followed by HA gene. We have also reported the new motifs for different virulence genes, which are further useful to design new detection and diagnosis kit for COVID -19.

Clinical Value of SARS-CoV2 IgM and IgG Antibodies in Diagnosis of COVID-19 in Suspected Cases.

OBJECTIVE: To explore the clinical value of SARS-CoV2 IgM and IgG antibodies in the diagnosis of COVID-19 in suspected cases by likelihood ratio. METHODS: By reinterpreting data from a previous study, the positive likelihood ratio of IgM and IgG antibodies in COVID-19 pneumonia diagnosis was calculated, and the posterior probability of IgM and IgG antibodies and their tandem detection was calculated finally. RESULTS: The positive likelihood ratios of single IgM and IgG antibodies were 18.50 and 12.65, respectively, and the posterior probabilities were 90.18% and 86.26%, respectively. However, the posterior probability of the two antibody-tandem test was 99.15%, which could give clinicians more quantitative confidence in the diagnosis of COVID-19 in suspected cases. According to the results of this study, combining the advantages and disadvantages of nucleic acid testing and antibody detection, a feasible clinical path was found for clinicians to diagnose COVID-19 pneumonia from suspected cases. CONCLUSION: For suspected cases, IgM- and IgG-antibody tests should first be done at the same time. If all antibody tests are positive, COVID-19 pneumonia could be confirmed. If not, nucleic acid detection (once or more) should be carried out, and in extreme cases high-throughput viral genome sequencing is required.